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Machine Learning for OpenCV

You're reading from   Machine Learning for OpenCV Intelligent image processing with Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781783980284
Length 382 pages
Edition 1st Edition
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Authors (2):
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Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Michael Beyeler (USD) Michael Beyeler (USD)
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Michael Beyeler (USD)
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Table of Contents (13) Chapters Close

Preface 1. A Taste of Machine Learning FREE CHAPTER 2. Working with Data in OpenCV and Python 3. First Steps in Supervised Learning 4. Representing Data and Engineering Features 5. Using Decision Trees to Make a Medical Diagnosis 6. Detecting Pedestrians with Support Vector Machines 7. Implementing a Spam Filter with Bayesian Learning 8. Discovering Hidden Structures with Unsupervised Learning 9. Using Deep Learning to Classify Handwritten Digits 10. Combining Different Algorithms into an Ensemble 11. Selecting the Right Model with Hyperparameter Tuning 12. Wrapping Up

Understanding the McCulloch-Pitts neuron

In 1943, Warren McCulloch and Walter Pitts published a mathematical description of neurons as they were believed to operate in the brain. A neuron receives input from other neurons through connections on its dendritic tree, which are integrated to produce an output at the cell body (or soma). The output is then communicated to other neurons via a long wire (or axon), which eventually branches out to make one or more connections (at axon terminals) on the dendritic tree of other neurons. An example neuron is shown in the following figure:

Schematic of a neuron (nerve cell). Adapted from a figure by Looxix at French Wikipedia (CC BY-SA 3.0)

McCulloch and Pitts described the inner workings of such a neuron as a simple logic gate that would be either on or off, depending on the input it receives on its dendritic tree. Specifically, the neuron...

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